tf-encrypted is a Python library built on top of TensorFlow for researchers and practitioners to experiment with privacy-preserving machine learning. It provides an interface similar to that of TensorFlow, and aims at making the technology readily available without first becoming an expert in machine learning, cryptography, distributed systems, and high performance computing.

In particular, the library focuses on:

Usability: The API and its underlying design philosophy make it easy to get started, use, and integrate privacy-preserving technology into pre-existing machine learning processes.

Extensibility: The architecture supports and encourages experimentation and benchmarking of new cryptographic protocols and machine learning algorithms.

Performance: Optimizing for tensor-based applications and relying on TensorFlow’s backend means runtime performance comparable to that of specialized stand-alone frameworks.

Community: With a primary goal of pushing the technology forward the project encourages collaboration and open source over proprietary and closed solutions.

Security: Cryptographic protocols are evaluated against strong notions of security and [known limitations](#known-limitations) are highlighted.

Checkout the Getting Started guide to learn how to get up and running with private machine learning.

You can view the project source, contribute, and asks questions on GitHub.